Learning pairwise-similarity guided sparse functional connectivity network for MCI Classification

Xiaobo Chen, Han Zhang, Yu Zhang, Zuoyong Li, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Learning functional connectivity (FC) network from resting-state function magnetic resonance imaging (RS-fMRI) data via sparse representation (SR) or weighted SR (WSR) has been proved to be promising for the diagnosis of Alzheimer's disease and its prodromal stage, mild cognitive impairment (MCI). However, traditional SR/WSR based approaches learn the representation of each brain region independently, without fully taking into account the possible relationship between brain regions. To remedy this limitation, we propose a novel FC modeling approach by considering two types of possible relationship between different brain regions which are incorporated into SR/WSR approaches in the form of regularization. In this way, the representations of all brain regions can be jointly learned. Furthermore, an efficient alternating optimization algorithm is also developed to solve the resulting model. Experimental results show that our proposed method not only outperforms SR and WSR in the diagnosis of MCI subjects, but also leads to the brain FC network with better modularity structure.

Original languageEnglish
Title of host publicationProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages923-928
Number of pages6
ISBN (Electronic)9781538633540
DOIs
Publication statusPublished - 2018 Dec 13
Externally publishedYes
Event4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China
Duration: 2017 Nov 262017 Nov 29

Publication series

NameProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017

Other

Other4th Asian Conference on Pattern Recognition, ACPR 2017
CountryChina
CityNanjing
Period17/11/2617/11/29

Keywords

  • Functional connectivity
  • Mild cognitive impairment
  • Resting-state fMRI
  • Sparse representation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Chen, X., Zhang, H., Zhang, Y., Li, Z., & Shen, D. (2018). Learning pairwise-similarity guided sparse functional connectivity network for MCI Classification. In Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017 (pp. 923-928). [8575945] (Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACPR.2017.147